Online Master of Engineering in Computer Engineering
Program Overview
Introduction to the Online Master of Engineering in Computer Engineering
The online Master of Engineering in Computer Engineering (MEng: CE) at Dartmouth is designed to reflect both current and emerging engineering challenges in industry. This program focuses on intelligent systems—machines that interact with the world via a combination of sensing, computing, and actuation. Students in this program will learn to engineer the sensing and computing components of intelligent systems.
Learning Experience
The online MEng: CE leverages an online education platform to deliver the curriculum, allowing students to benefit from interactive video transcription, in-course note taking, and seamless learning across multiple devices—at a schedule and pace that best fits their life. Online courses include readings, video lectures, assignments, and discussion forums that help spark connections with peers.
Study on Your Own Schedule
- Dive deep with high-quality, pre-recorded lectures at a time that fits work and personal schedules.
- Personal faculty support: Ask questions and get one-on-one support from faculty and teaching assistants during virtual office hours.
- Peers from around the world: Learn and connect with classmates from around the world who bring global perspectives to each course.
Learning Objectives
Through the program, students will learn to:
- Extract information from data using a combination of broadly-applicable tools and task-specific techniques such as signal processing, machine learning, and machine vision.
- Implement information-extracting algorithms that fit within the constraints—and utilize the capabilities—of specialized computer hardware for intelligent systems.
- Design, analyze, build, test, and debug sensing and computing components of intelligent systems.
- Collaborate on projects with geographically-diverse team members.
Required Courses
The online MEng: CE requires a total of nine courses, including a capstone course. Students may take one or two courses at a time (two courses is considered a full-time course load).
Course Groups
- Extracting Information from Data:
- ENGG 408: Machine Learning (must be taken early)
- ENGG 410: Signal Processing (must be taken early)
- ENGG 417: Machine Vision
- ENGG 418: Applied Natural Language Processing
- ENGG 419: Deep Learning
- Hardware for Intelligent Systems:
- ENGG 415: Distributed Computing
- ENGG 462: Embedded Systems
- ENGG 463: Advanced FPGA Design
- Capstone:
- ENGG 499: Smart Sensors
Sample Course Plans
These sample course plans provide examples of how students might progress through the program either part-time or full-time with a Fall or Spring term start.
Part-Time Option, with Continuous Enrollment: Fall Term Start (27 months)
| Term | Courses |
|---|---|
| Year 1, Fall | ENGG 408: Machine Learning |
| Year 1, Winter | ENGG 463: Advanced FPGA Design |
| Year 1, Spring | ENGG 410: Signal Processing |
| Year 1, Summer | ENGG 419: Deep Learning |
| Year 2, Fall | ENGG 418: Applied Natural Language Processing |
| Year 2, Winter | ENGG 415: Distributed Computing |
| Year 2, Spring | ENGG 417: Machine Vision |
| Year 2, Summer | ENGG 462: Embedded Systems |
| Year 3, Fall | ENGG 499: Smart Sensors (Capstone) |
Full-Time Option, with Continuous Enrollment: Fall Term Start (15 months)
| Term | Courses |
|---|---|
| Year 1, Fall | ENGG 408: Machine Learning, ENGG 410: Signal Processing |
| Year 1, Winter | ENGG 463: Advanced FPGA Design, ENGG 415: Distributed Computing |
| Year 1, Spring | ENGG 417: Machine Vision |
| Year 1, Summer | ENGG 462: Embedded Systems, ENGG 419: Deep Learning |
| Year 2, Fall | ENGG 418: Applied Natural Language Processing, ENGG 499: Smart Sensors (Capstone) |
Faculty
- Eugene Santos Jr.: Professor of Engineering, Faculty Director, Master of Engineering Program
- Kofi M. Odame: Associate Professor of Engineering, Program Area Lead, Electrical and Computer Engineering
- Peter Chin: Professor of Engineering
- Kelly Seals: Professor of Engineering
- Kendall Farnham: Assistant Professor of Engineering
- Michael Kokko: Assistant Professor of Engineering, Director, Instructional Labs
- Jason Dahlstrom: Adjunct Assistant Professor of Engineering
- Tucker "Emme" Burgin: Assistant Professor of Engineering
Conclusion
The online Master of Engineering in Computer Engineering at Dartmouth offers a comprehensive program designed to equip students with the knowledge and skills necessary to drive the next generation of computer engineering and technology. With a focus on intelligent systems, students will learn to engineer the sensing and computing components of these systems, preparing them for careers in a rapidly evolving field.